from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 prediction (58.33%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	e70d8e7886766d41f30889506baa1e67	2cf49829391aa7891e877f2ed070adf0	2.037486	0.160038	NaN	0.000393	0.002037	brute	-1	1	0.663	0.185754	0.003161	0.687	10.968758	10.970346	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	22d5eac4a4146149b35eae6914921514	2cf49829391aa7891e877f2ed070adf0	2.840747	0.033275	NaN	0.000282	0.002841	brute	-1	5	0.757	0.188271	0.001007	0.742	15.088581	15.088797	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	2.063801	0.004240	NaN	0.000388	0.002064	brute	1	100	0.882	0.230377	0.007822	0.875	8.958368	8.963530	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	0.019551	0.000110	NaN	0.000041	0.019551	brute	1	100	1.000	0.008942	0.000266	0.000	2.186471	2.187441	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	2.858204	0.019720	NaN	0.000280	0.002858	brute	-1	100	0.882	0.228269	0.001995	0.875	12.521228	12.521707	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	0.024168	0.003516	NaN	0.000033	0.024168	brute	-1	100	1.000	0.009336	0.001486	0.000	2.588690	2.621270	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	efac583623711d395ddae7a1e881ff68	2cf49829391aa7891e877f2ed070adf0	2.064481	0.028765	NaN	0.000388	0.002064	brute	1	5	0.757	0.188912	0.002451	0.742	10.928284	10.929203	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	b79d63bb7670c492de5c3befac58fe29	2cf49829391aa7891e877f2ed070adf0	1.183130	0.005814	NaN	0.000676	0.001183	brute	1	1	0.663	0.187264	0.002336	0.687	6.317962	6.318454	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	e70d8e7886766d41f30889506baa1e67	88843f54689e3271092f70126e1de585	1.815722	0.028080	NaN	0.000009	0.001816	brute	-1	1	0.896	0.028371	0.000520	0.967	63.999399	64.010166	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	22d5eac4a4146149b35eae6914921514	88843f54689e3271092f70126e1de585	2.707666	0.022798	NaN	0.000006	0.002708	brute	-1	5	0.922	0.029064	0.000476	0.974	93.160682	93.173190	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	16f94fc93942ff7ece860c9b0f64645f	88843f54689e3271092f70126e1de585	1.953524	0.001538	NaN	0.000008	0.001954	brute	1	100	0.929	0.066193	0.002342	0.975	29.512759	29.531232	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2d5f2b2c02d77766434d9e5033b4a76c	88843f54689e3271092f70126e1de585	2.741653	0.030596	NaN	0.000006	0.002742	brute	-1	100	0.929	0.065436	0.001631	0.975	41.898437	41.911457	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	efac583623711d395ddae7a1e881ff68	88843f54689e3271092f70126e1de585	1.942469	0.004236	NaN	0.000008	0.001942	brute	1	5	0.922	0.029181	0.000577	0.974	66.567041	66.580063	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	b79d63bb7670c492de5c3befac58fe29	88843f54689e3271092f70126e1de585	1.070771	0.003015	NaN	0.000015	0.001071	brute	1	1	0.896	0.027912	0.000561	0.967	38.362752	38.370504	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.147	0.0	-1	1	0.048	0.003	0.234	0.235	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.193	0.0	-1	5	0.047	0.000	0.236	0.237	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.139	0.0	1	100	0.047	0.000	0.238	0.238	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.016	0.006	5.082	0.0	-1	100	0.047	0.000	0.335	0.335	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.221	0.0	1	5	0.047	0.000	0.236	0.236	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.145	0.0	1	1	0.047	0.000	0.238	0.238	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.376	0.0	-1	1	0.008	0.000	0.508	0.508	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.370	0.0	-1	5	0.008	0.000	0.513	0.513	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.379	0.0	1	100	0.008	0.000	0.500	0.500	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.375	0.0	-1	100	0.008	0.000	0.509	0.509	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.376	0.0	1	5	0.008	0.000	0.506	0.506	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.375	0.0	1	1	0.008	0.000	0.506	0.507	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.037	0.160	0.000	0.002	-1	1	0.186	0.003	10.969	10.970	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.001	0.000	0.021	-1	1	0.009	0.000	2.401	2.401	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.841	0.033	0.000	0.003	-1	5	0.188	0.001	15.089	15.089	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.001	0.000	0.023	-1	5	0.009	0.000	2.591	2.591	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.064	0.004	0.000	0.002	1	100	0.230	0.008	8.958	8.964	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.000	0.000	0.020	1	100	0.009	0.000	2.186	2.187	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.858	0.020	0.000	0.003	-1	100	0.228	0.002	12.521	12.522	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.004	0.000	0.024	-1	100	0.009	0.001	2.589	2.621	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.064	0.029	0.000	0.002	1	5	0.189	0.002	10.928	10.929	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.000	0.000	0.020	1	5	0.009	0.000	2.190	2.191	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.183	0.006	0.001	0.001	1	1	0.187	0.002	6.318	6.318	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.018	0.000	0.000	0.018	1	1	0.009	0.000	2.057	2.057	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.816	0.028	0.000	0.002	-1	1	0.028	0.001	63.999	64.010	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.009	0.005	0.000	0.009	-1	1	0.001	0.000	12.520	12.593	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.708	0.023	0.000	0.003	-1	5	0.029	0.000	93.161	93.173	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.002	0.000	0.006	-1	5	0.001	0.000	7.676	7.699	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.954	0.002	0.000	0.002	1	100	0.066	0.002	29.513	29.531	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.795	3.810	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.742	0.031	0.000	0.003	-1	100	0.065	0.002	41.898	41.911	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.005	0.000	0.007	-1	100	0.001	0.000	8.877	8.942	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.942	0.004	0.000	0.002	1	5	0.029	0.001	66.567	66.580	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.136	4.158	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.071	0.003	0.000	0.001	1	1	0.028	0.001	38.363	38.371	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.630	2.642	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	fe8267e25dadb302ed498e50fabf7ef4	1eb6cf1a720a225efb91df0b529b0510	0.795165	0.983172	NaN	0.000101	0.000795	kd_tree	-1	1	0.929	0.123675	0.004460	0.910	6.429500	6.433679	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	dc74b969bf622bc24ba3bc62c980983b	1eb6cf1a720a225efb91df0b529b0510	1.050018	0.315225	NaN	0.000076	0.001050	kd_tree	-1	5	0.946	0.240553	0.010176	0.941	4.365021	4.368925	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	41accfcdcd5c50784bedf6164b63de99	1eb6cf1a720a225efb91df0b529b0510	5.603861	0.406256	NaN	0.000014	0.005604	kd_tree	1	100	0.951	0.704055	0.006573	0.940	7.959412	7.959759	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	022f7445d43bb1dbc24dc3106c03cb93	1eb6cf1a720a225efb91df0b529b0510	3.127008	0.108438	NaN	0.000026	0.003127	kd_tree	-1	100	0.951	0.676374	0.006511	0.940	4.623195	4.623409	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	84622ba45e941db642965553529e1941	1eb6cf1a720a225efb91df0b529b0510	1.678828	0.200153	NaN	0.000048	0.001679	kd_tree	1	5	0.946	0.231535	0.003826	0.941	7.250853	7.251843	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	a9455565db1d8e052a783317c99744ff	1eb6cf1a720a225efb91df0b529b0510	0.956498	0.273766	NaN	0.000084	0.000956	kd_tree	1	1	0.929	0.124604	0.001311	0.910	7.676307	7.676732	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	fe8267e25dadb302ed498e50fabf7ef4	edf7cb014c2c0910e1c3aaaf8ae633f2	0.023352	0.010450	NaN	0.000685	0.000023	kd_tree	-1	1	0.891	0.000369	0.000080	0.879	63.332646	64.807040	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	dc74b969bf622bc24ba3bc62c980983b	edf7cb014c2c0910e1c3aaaf8ae633f2	0.020542	0.000941	NaN	0.000779	0.000021	kd_tree	-1	5	0.911	0.000613	0.000036	0.905	33.522498	33.581772	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	41accfcdcd5c50784bedf6164b63de99	edf7cb014c2c0910e1c3aaaf8ae633f2	0.034418	0.005652	NaN	0.000465	0.000034	kd_tree	1	100	0.894	0.004355	0.000120	0.917	7.903084	7.906094	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	022f7445d43bb1dbc24dc3106c03cb93	edf7cb014c2c0910e1c3aaaf8ae633f2	0.035195	0.005084	NaN	0.000455	0.000035	kd_tree	-1	100	0.894	0.005130	0.001452	0.917	6.860791	7.130146	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	84622ba45e941db642965553529e1941	edf7cb014c2c0910e1c3aaaf8ae633f2	0.017327	0.000102	NaN	0.000923	0.000017	kd_tree	1	5	0.911	0.000610	0.000046	0.905	28.398379	28.477641	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	a9455565db1d8e052a783317c99744ff	edf7cb014c2c0910e1c3aaaf8ae633f2	0.016274	0.000151	NaN	0.000983	0.000016	kd_tree	1	1	0.891	0.000396	0.000066	0.879	41.117383	41.684272	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.133	0.035	0.026	0.0	-1	1	0.801	0.052	3.912	3.920	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.764	0.040	0.021	0.0	-1	5	0.781	0.012	4.819	4.819	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.838	0.074	0.021	0.0	1	100	0.753	0.014	5.096	5.097	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.550	0.091	0.023	0.0	-1	100	0.789	0.019	4.497	4.498	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.568	0.076	0.022	0.0	1	5	0.755	0.008	4.729	4.729	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.754	0.043	0.021	0.0	1	1	0.783	0.008	4.793	4.793	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.017	0.0	-1	1	0.004	0.004	0.245	0.347	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.000	0.000	0.034	0.0	-1	5	0.002	0.001	0.242	0.296	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	100	0.001	0.000	0.552	0.555	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	100	0.001	0.000	0.596	0.597	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.000	0.000	0.033	0.0	1	5	0.001	0.000	0.525	0.528	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.000	0.000	0.033	0.0	1	1	0.001	0.000	0.565	0.565	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.795	0.983	0.000	0.001	-1	1	0.124	0.004	6.430	6.434	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	9.258	9.569	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.050	0.315	0.000	0.001	-1	5	0.241	0.010	4.365	4.369	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	7.423	7.913	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.604	0.406	0.000	0.006	1	100	0.704	0.007	7.959	7.960	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	3.732	3.892	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.127	0.108	0.000	0.003	-1	100	0.676	0.007	4.623	4.623	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.199	6.485	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.679	0.200	0.000	0.002	1	5	0.232	0.004	7.251	7.252	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	5	0.000	0.000	3.111	3.285	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.956	0.274	0.000	0.001	1	1	0.125	0.001	7.676	7.677	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.222	3.440	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.010	0.001	0.000	-1	1	0.000	0.000	63.333	64.807	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	25.329	26.805	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.021	0.001	0.001	0.000	-1	5	0.001	0.000	33.522	33.582	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	22.209	23.444	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.034	0.006	0.000	0.000	1	100	0.004	0.000	7.903	7.906	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.917	6.270	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.035	0.005	0.000	0.000	-1	100	0.005	0.001	6.861	7.130	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	19.676	21.228	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.017	0.000	0.001	0.000	1	5	0.001	0.000	28.398	28.478	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.492	6.981	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.016	0.000	0.001	0.000	1	1	0.000	0.000	41.117	41.684	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	5.968	6.407	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.518	0.060	30	0.031	0.0	random	0.410	0.029	1.262	1.266	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.575	0.015	30	0.028	0.0	k-means++	0.449	0.030	1.280	1.283	See	See
6	KMeans_tall	fit	1000000	1000000	100	6.302	0.328	30	0.127	0.0	random	2.921	0.026	2.158	2.158	See	See
9	KMeans_tall	fit	1000000	1000000	100	6.368	0.051	30	0.126	0.0	k-means++	3.090	0.028	2.061	2.061	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.001	0.000	30	0.011	0.000	random	0.0	0.0	8.910	12.415	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	8.750	12.945	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	k-means++	0.0	0.0	11.034	12.328	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	0.0	0.0	12.111	13.303	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.476	0.000	random	0.0	0.0	5.981	6.407	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	12.333	12.636	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.493	0.000	k-means++	0.0	0.0	6.014	6.397	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	11.200	11.497	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 prediction (50.0%). The observed differences can be found in the diff_adjusted_rand_scores column. The chosen difference threshold is 0.001 for adjusted_rand_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	3db9c030f488d0e8ff350ae449da4627	058b7e3842b587a5c675518f0706f5ee	0.001728	0.000094	20	0.009258	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000507	0.000038	-0.000965	3.411913	3.421573	0.001090
4	KMeans_short	predict	10000	1000	2	ac9b8cec4749455c0d1cd1a92c150716	058b7e3842b587a5c675518f0706f5ee	0.001752	0.000117	20	0.009132	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000497	0.000035	-0.000750	3.524897	3.533599	0.001995
7	KMeans_short	predict	10000	1000	100	3db9c030f488d0e8ff350ae449da4627	7f85b913f395ec54101a6738ea63a9a7	0.002619	0.000256	20	0.305427	0.000003	full	random	20	300	1	1.000000e-16	0.278733	0.001042	0.000099	0.293767	2.512904	2.524307	0.015034
10	KMeans_short	predict	10000	1000	100	ac9b8cec4749455c0d1cd1a92c150716	7f85b913f395ec54101a6738ea63a9a7	0.002463	0.000166	20	0.324861	0.000002	full	k-means++	20	300	1	1.000000e-16	0.317011	0.001089	0.000093	0.256968	2.260661	2.268859	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.082	0.001	20	0.002	0.0	random	0.030	0.002	2.725	2.729	See	See
3	KMeans_short	fit	10000	10000	2	0.239	0.003	20	0.001	0.0	k-means++	0.082	0.001	2.902	2.903	See	See
6	KMeans_short	fit	10000	10000	100	0.225	0.005	20	0.036	0.0	random	0.124	0.002	1.815	1.816	See	See
9	KMeans_short	fit	10000	10000	100	0.619	0.011	20	0.013	0.0	k-means++	0.324	0.008	1.908	1.909	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.001	0.0	3.412	3.422	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	13.473	13.814	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.000	0.0	3.525	3.534	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	12.802	13.215	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.305	0.000	random	0.001	0.0	2.513	2.524	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	8.803	8.912	See	See
10	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.325	0.000	k-means++	0.001	0.0	2.261	2.269	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	0.000	0.0	9.020	9.214	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	f11be2529b0a77bc8fb5ee8de141e6b2	09c36ed6521e03974d0c134895f56c01	0.000373	0.000329	[20]	2.147105	3.725947e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.001442	0.003395	0.55	0.258418	0.661014	0.01
4	LogisticRegression	predict	1000	100	10000	f11be2529b0a77bc8fb5ee8de141e6b2	7daecaea01e5baa8c862f264e3c1c3b0	0.001828	0.000343	[26]	4.375858	1.828213e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.004849	0.000794	0.28	0.377011	0.382034	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.279	0.386	[20]	0.071	0.000	2.053	0.031	5.495	5.495	See	See
3	LogisticRegression	fit	1000	1000	10000	0.957	0.498	[26]	0.084	0.001	0.990	0.036	0.967	0.968	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.147	0.0	0.001	0.003	0.258	0.661	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.014	0.0	0.000	0.000	0.423	0.431	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	4.376	0.0	0.005	0.001	0.377	0.382	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.042	0.0	0.001	0.000	0.053	0.053	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). The observed differences can be found in the diff_r2_scores column. The chosen difference threshold is 0.001 for r2_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	diff_r2_scores
1	Ridge	predict	1000	1000	10000	a70ae8dc5c059ec88cf27c61016aee2a	14fae294d530397144c65ba3209bf125	0.011756	0.000117	NaN	6.805114	0.000012	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.018804	0.000152	0.122191	0.625195	0.625216	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.186	0.004	0.430	0.0	0.187	0.002	0.993	0.993	See	See
3	Ridge	fit	1000000	1000000	100	1.129	0.048	0.708	0.0	0.315	0.255	3.586	4.614	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.0	6.805	0.0	0.019	0.0	0.625	0.625	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	1.216	0.0	0.000	0.0	0.797	0.879	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	5.766	0.0	0.000	0.0	0.431	0.623	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	0.019	0.0	0.000	0.0	0.581	0.618	See	See